CO vs COiMG in control samples

PCA plots

#pca plots
PCAplot(pca.ctr, "Line", Shape = "COiMg", PoV.df=PoV.ctr, pc.1 = 1, pc.2 = 2) #M30 & M33 are outleirs, let's remove them for now

PCAplot(pca.ctr, "condition", Shape = "Line", PoV.df=PoV.ctr, pc.1 = 1, pc.2 = 2) #M30 & M33 are outleirs, let's remove them for now

PCAplot(pca.ctr, "COiMg", Shape = "Line", PoV.df=PoV.ctr, pc.1 = 1, pc.2 = 2) #M30 & M33 are outleirs, let's remove them for now

Volcano plot

volcano_plot(data.frame(new_coimg), title = 'CO vs COiMg',
             annotate_by=unlist(AB.genes), ymax1 = 30, ymax2 = 31, xmax1 = -5, xmax2 = 8) + 
  scale_x_continuous(breaks = c(-5,-3,-1, 0,1,3,5),
                     labels = c("-5",
                                '-3',
                                "-1",
                                "0",
                                "1",
                                '3',
                                "5"))

Boxplot genes of interest

meta.ctr$Organoids <- NA
meta.ctr[meta.ctr$COiMg %in% 'yes',]$Organoids <- 'COiMG'
meta.ctr[meta.ctr$COiMg %in% 'no',]$Organoids <- 'CO'

boxplot1 <- data.frame('Sample'=rownames(meta.ctr),'Organoids'=meta.ctr$Organoids,t(batch.ctr[rownames(batch.ctr) %in% c('C3',"C1QA","CX3CR1","TLR4"),]))
boxplot2 <- data.frame('Sample'=rownames(meta.ctr),'Organoids'=meta.ctr$Organoids,t(batch.ctr[rownames(batch.ctr) %in% c("CD68","TREM2",'TYROBP',"SYK"),]))
boxplot3 <- data.frame('Sample'=rownames(meta.ctr),'Organoids'=meta.ctr$Organoids,t(batch.ctr[rownames(batch.ctr) %in% c("SPP1","CSF1R","IRF8"),]))

# Reshape the data using gather
expression_1 <- gather(boxplot1, key = "Gene", value = "Expression", -Sample, -Organoids)
expression_2 <- gather(boxplot2, key = "Gene", value = "Expression", -Sample, -Organoids)
expression_3 <- gather(boxplot3, key = "Gene", value = "Expression", -Sample, -Organoids)


ggplot(expression_1,aes(x = Gene, y = Expression, fill = Organoids)) +
  geom_boxplot(position = position_dodge(width = 0.8), width = 0.3, alpha = 0.8, outlier.shape = NA)  +
  geom_point(aes(fill = Organoids), size = 3, shape = 21, position = position_jitterdodge(dodge.width = 0.8, jitter.height = 0.2, jitter.width = 0.01)) +
  theme_minimal() + 
  scale_fill_manual(values=c("white","black")) +
  labs(y = "Standardized expression")

ggplot(expression_2,aes(x = Gene, y = Expression, fill = Organoids)) +
  geom_boxplot(position = position_dodge(width = 0.8), width = 0.3, alpha = 0.8, outlier.shape = NA)  +
  geom_point(aes(fill = Organoids), size = 3, shape = 21, position = position_jitterdodge(dodge.width = 0.8, jitter.height = 0.2, jitter.width = 0.01)) +
  theme_minimal() + 
  scale_fill_manual(values=c("white","black")) +
  labs(y = "Standardized expression")

ggplot(expression_3,aes(x = Gene, y = Expression, fill = Organoids)) +
  geom_boxplot(position = position_dodge(width = 0.8), width = 0.3, alpha = 0.8, outlier.shape = NA)  +
  geom_point(aes(fill = Organoids), size = 3, shape = 21, position = position_jitterdodge(dodge.width = 0.8, jitter.height = 0.2, jitter.width = 0.01)) +
  theme_minimal() + 
  scale_fill_manual(values=c("white","black")) +
  labs(y = "Standardized expression")

Patir microglia genes heatmap

ra.ctr <- HeatmapAnnotation(
  Organoids = meta.ctr$Organoids,
  col = list(
    Organoids = c("COiMG"='black','CO'='white')),
  show_annotation_name = T,
  show_legend = T)


ComplexHeatmap::Heatmap(t(hm_counts.ctr),
                        cluster_rows = T,
                        cluster_columns = T,
                        show_row_dend = T,
                        show_column_dend = T,
                        show_row_names = F,
                        show_column_names = F,
                        top_annotation = ra.ctr,
                        right_annotation = rowAnnotation(
                          text = anno_text(colnames(hm_counts.ctr), rot = 0, just = "left",gp=gpar(fontsize=8))),
                        col = colorRampPalette(rev(RColorBrewer::brewer.pal(9,"RdBu")))(10),
                        name = "Z-score expression")

## Enrichment heatmap (gene sets of interest)

ComplexHeatmap::Heatmap(DEG.enrich_coimg[!rownames(DEG.enrich_coimg) %in% 'Lipid metabolism',],
                        cluster_rows = F,
                        cluster_columns = F,
                        show_row_dend = F,
                        show_column_dend = F,
                        show_row_names = T,
                        col = colorRampPalette(RColorBrewer::brewer.pal(9,"Blues"))(30),
                        name = "log2(q)",
)

GO enrichment

dotplot(new_coimg.upregGO, split="ONTOLOGY", showCategory=10) + facet_grid(ONTOLOGY~., scale="free")

dotplot(new_coimg.downregGO, split="ONTOLOGY", showCategory=10) + facet_grid(ONTOLOGY~., scale="free")

IFNy CO vs COiMG

ctr_CO <- batch.rem3_mod1[,colnames(batch.rem3_mod1) %in% rownames(meta[meta$COiMg %in% "no" & meta$condition %in% "IFNg",])]
ctr_COiMG <- batch.rem3_mod1[,colnames(batch.rem3_mod1) %in% rownames(meta[meta$COiMg %in% "yes" & meta$condition %in% "IFNg",])]
cor(ctr_CO,ctr_COiMG)

##           M13       M14       M15       M21       M27       M33        F9
## M16 0.9888853 0.9750494 0.9854099 0.9860980 0.9844882 0.9665080 0.9772695
## M17 0.9882932 0.9689689 0.9821487 0.9803589 0.9830655 0.9582293 0.9703453
## M18 0.9853977 0.9796923 0.9773084 0.9801409 0.9848437 0.9628487 0.9773172
## M24 0.9893057 0.9799679 0.9870001 0.9855227 0.9831987 0.9708646 0.9793361
## M30 0.9839439 0.9849775 0.9782459 0.9812581 0.9760353 0.9881220 0.9857833
## M36 0.9808666 0.9739084 0.9819043 0.9848451 0.9726188 0.9723869 0.9735683
## F15 0.9896431 0.9848594 0.9858859 0.9880105 0.9875725 0.9839841 0.9898616
## F16 0.9736695 0.9781072 0.9730415 0.9772165 0.9707696 0.9863550 0.9842931
## F17 0.9837642 0.9760274 0.9815035 0.9841363 0.9823853 0.9590486 0.9744931
##           F10
## M16 0.9820133
## M17 0.9788688
## M18 0.9814824
## M24 0.9853527
## M30 0.9823925
## M36 0.9794674
## F15 0.9870064
## F16 0.9753547
## F17 0.9794916

df1 <- data.frame('CO'=rowMeans(ctr_CO))
df2 <- data.frame('COiMG'=rowMeans(ctr_COiMG))

df.plot <- cbind(df1,df2)

quantile(as.matrix(df1-df2))

##          0%         25%         50%         75%        100% 
## -5.52300947 -0.16864063  0.01132637  0.16271150  1.86109744

rownames(df.plot)[df1-df2 >= 1 | df1-df2 <= -4]

##  [1] "DLX6"    "NOS2"    "TFAP2B"  "MGST1"   "PAX7"    "KITLG"   "ASIC4"  
##  [8] "NAALAD2" "MOXD1"   "LRP2"    "SLC32A1" "TOX3"    "WNT3"    "FZD10"  
## [15] "SPP1"    "ONECUT2" "PAX3"    "GAD2"    "ASCL1"   "ZFHX3"   "GREB1L" 
## [22] "C1QL2"   "DLX1"    "SFRP2"   "TENM2"   "CDH8"    "POU4F1"  "ZIC1"   
## [29] "ROBO3"   "WNT7A"   "ZIC3"    "H4C8"    "VCAM1"   "NDST3"   "CRABP1" 
## [36] "PTF1A"   "GBX2"    "SHOX2"   "IRX1"    "IRX2"    "HSPA6"   "LRRN3"  
## [43] "WFIKKN2" "ZIC4"    "PRIMA1"  "IRX5"    "OLIG3"   "IRX3"    "GSX2"   
## [50] "FIGN"    "BGN"     "SOX1"    "POU3F2"  "PCDH18"  "GREB1"   "POU3F4" 
## [57] "HES5"    "TOX"     "HSPA1B"  "HSPA1A"  "VGLL3"   "TRIM71"  "UBD"    
## [64] "SP9"     "OR2I1P"  "H4C14"   "H2BC8"

Gene <- ifelse(rownames(df.plot) %in% rownames(df.plot)[df1-df2 >= 1 | df1-df2 <= -4], rownames(df.plot), "")

ggplot(df.plot, aes(x=CO,y=COiMG))+
  geom_point() +
  labs(title = "Expression overlap CO vs COiMG under IFNy stimulation",
       x = "Standardized CO counts",
       y = "Standardized COiMG counts") +
  geom_smooth(method=lm,  linetype="dashed",
             color="darkred", fill="blue", se=TRUE) +
  geom_point(shape=18, color="grey")+
  stat_cor(method = "pearson", label.x = 0, label.y = 20)+
  geom_text_repel(data = df.plot, aes(x = CO, y = COiMG, label = Gene), vjust = 3, size = 3) +
  theme_minimal()

Stimulation (IFNy) samples

PCA plots

PCAplot(pca.ifny, "condition", Shape = "Line", PoV.df=PoV.ifny, pc.1 = 1, pc.2 = 2) #M30 & M33 are outleirs, let's remove them for now

Volcano plot

new_IFNy$symbol <- rownames(new_IFNy)
top30.new_IFNy <- c(rownames(new_IFNy[order(new_IFNy$log2FoldChange),][new_IFNy[order(new_IFNy$log2FoldChange),]$padj < 0.05,][1:15,]),
                    rownames(new_IFNy[order(new_IFNy$log2FoldChange, decreasing = T),][new_IFNy[order(new_IFNy$log2FoldChange, decreasing = T),]$padj < 0.05,][1:15,]))

volcano_plot(data.frame(new_IFNy), title = 'ctr vs IFNy',
             annotate_by=top30.new_IFNy, ymax1 = 60, ymax2 = 61, xmax1 = -5, xmax2 = 10) + 
  scale_x_continuous(breaks = c(-5,-3,-1, 0,1,3,5),
                     labels = c("-5",
                                '-3',
                                "-1",
                                "0",
                                "1",
                                '3',
                                "5"))

Boxplot genes of interest

for (i in unique(meta.CO$Line)){
  boxplot.g_ifny_byline <- boxplot.g_ifny[boxplot.g_ifny$Sample %in% rownames(meta.CO[!meta.CO$condition %in% 'LPS',][meta.CO[!meta.CO$condition %in% 'LPS',]$Line %in% i,]),]
  expression_data_long.ifny_byline <- gather(boxplot.g_ifny_byline, key = "Gene", value = "Expression", -Sample, -Stimulation)
  p <- ggplot(expression_data_long.ifny_byline,aes_string(x = 'Gene', y = 'Expression', fill = 'Stimulation')) +
    geom_boxplot(position = position_dodge(width = 0.8), width = 0.7)  +
    labs(y=paste('Expression in',i)) + 
    theme_minimal()
  plot(p)
}

boxplot.g_ifny_down <- boxplot.g_ifny[boxplot.g_ifny$Sample %in% rownames(meta.CO[!meta.CO$condition %in% 'LPS',]),]
boxplot.g_ifny_down <- boxplot.g_ifny_down[,colnames(boxplot.g_ifny_down) %in% c("CLDN5","IGF1","Stimulation","Sample")]
boxplot.g_ifny_down <- gather(boxplot.g_ifny_down, key = "Gene", value = "Expression", -Sample, -Stimulation)

boxplot.g_ifny_up <- boxplot.g_ifny[boxplot.g_ifny$Sample %in% rownames(meta.CO[!meta.CO$condition %in% 'LPS',]),]
boxplot.g_ifny_up <- boxplot.g_ifny_up[,!colnames(boxplot.g_ifny_up) %in% c("CLDN5","IGF1")]
boxplot.g_ifny_up <- gather(boxplot.g_ifny_up, key = "Gene", value = "Expression", -Sample, -Stimulation)


ggplot(boxplot.g_ifny_down,aes(x = Gene, y = Expression, fill = Stimulation)) +
  geom_boxplot(position = position_dodge(width = 0.8), width = 0.3, alpha = 0.8, outlier.shape = NA)  +
  geom_point(aes(fill = Stimulation), size = 3, shape = 21, position = position_jitterdodge(dodge.width = 0.8, jitter.height = 0.2, jitter.width = 0.01)) +
  theme_minimal() + 
  scale_fill_manual(values=c("white","black")) +
  labs(y = "Standardized expression")

ggplot(boxplot.g_ifny_up,aes(x = Gene, y = Expression, fill = Stimulation)) +
  geom_boxplot(position = position_dodge(width = 0.8), width = 0.3, alpha = 0.8, outlier.shape = NA)  +
  geom_point(aes(fill = Stimulation), size = 3, shape = 21, position = position_jitterdodge(dodge.width = 0.8, jitter.height = 0.2, jitter.width = 0.01)) +
  theme_minimal() + 
  scale_fill_manual(values=c("white","black")) +
  labs(y = "Standardized expression")

Top 40 DEGs heatmap

ra.ifny <- HeatmapAnnotation(
  Condition = meta.CO[!meta.CO$condition %in% 'LPS',]$condition,
  col = list(
    Condition = c("IFNg"='#98FB98','ctrl'='white')),
  show_annotation_name = T,
  show_legend = T)

top50.new_IFNy <- c(rownames(new_IFNy[order(new_IFNy$log2FoldChange),][new_IFNy[order(new_IFNy$log2FoldChange),]$padj < 0.05,][1:25,]),
                    rownames(new_IFNy[order(new_IFNy$log2FoldChange, decreasing = T),][new_IFNy[order(new_IFNy$log2FoldChange, decreasing = T),]$padj < 0.05,][1:25,]))

hm_counts.ifny <- t(scale(t(batch.ifny[rownames(batch.ifny) %in% top50.new_IFNy,])))

ComplexHeatmap::Heatmap(hm_counts.ifny,
                        cluster_rows = T,
                        cluster_columns = T,
                        show_row_dend = T,
                        show_column_dend = T,
                        show_row_names = F,
                        show_column_names = F,
                        top_annotation = ra.ifny,
                        right_annotation = rowAnnotation(
                          text = anno_text(rownames(hm_counts.ifny), rot = 0, just = "left",gp=gpar(fontsize=8))),
                        col = colorRampPalette(rev(RColorBrewer::brewer.pal(9,"RdBu")))(10),
                        name = "Z-score expression")

Enrichment heatmap (gene sets of interest)

ComplexHeatmap::Heatmap(DEG.enrich_ifny[!rownames(DEG.enrich_ifny) %in% 'Lipid metabolism',],
                        cluster_rows = F,
                        cluster_columns = F,
                        show_row_dend = F,
                        show_column_dend = F,
                        show_row_names = T,
                        col = colorRampPalette(RColorBrewer::brewer.pal(9,"Blues"))(30),
                        name = "log2(q)",
)

NDD gene set enrichment in IFNy

cgenes <- readxl::read_xlsx('/Users/kubler01/Documents/R projects/gene lists/ndd/Gene lists for organoid paper.xlsx', col_names = T)
m <- data.frame(t(toupper(na.omit(cgenes$`ADHD GWAS associated genes through FUMA`))[toupper(na.omit(cgenes$`ADHD GWAS associated genes through FUMA`)) %in% rownames(batch.ifny)]), check.names = F)
for(i in names(cgenes)[-1]){
  k <- na.omit(cgenes[i])
  s <- k[as.matrix(k)[,1] %in% rownames(batch.ifny),]
  m <- rbind.fill(data.frame(m),data.frame(t(s)))
}
rownames(m) <- names(cgenes)
m <- t(m)

#extract DEGs
DEGs.ifny <- list("Up-regulated"=na.omit(all_res$IFNg$DEGs[,1]),"Down-regulated"=na.omit(all_res$IFNg$DEGs[,2]))


#gene set enrichment analysis:
ndd.enrich <- matrix(nrow=2*length(colnames(m)), ncol=6)
ndd.rep <- NULL
for (i in colnames(m))
  ndd.rep <- c(ndd.rep, paste(i, "_up", sep=""), paste(i, "_down", sep=""))
rownames(ndd.enrich) <- ndd.rep
colnames(ndd.enrich) <- colnames(GSEA.byMod(mod.gl=DEGs.ifny, m[,1], universe=35324))


for (x in colnames(m)){
  ndd.enrich[gsub("*_up","",gsub("*_down","",rownames(ndd.enrich))) %in% x,] <- as.matrix(GSEA.byMod(mod.gl=DEGs.ifny, na.omit(as.matrix(m)[,x]), universe=na.omit(rownames(res))))
}

ndd.enrich <- data.frame(ndd.enrich)
ndd.enrich$log.q <- -log2(ndd.enrich[,6])

#DEG.enrich.res[is.infinite(DEG.enrich.res$log.q),]$log.q <- 1
ndd.enrich.ress <- cbind("Upregulated"=ndd.enrich[c(seq(1, length(rownames(ndd.enrich))-1, by = 2)),][,7],
                    "Downregulated"=ndd.enrich[c(seq(2, length(rownames(ndd.enrich)), by = 2)),][,7])
rownames(ndd.enrich.ress) <- colnames(m)

ComplexHeatmap::Heatmap(ndd.enrich.ress,
                        cluster_rows = F,
                        cluster_columns = F,
                        show_row_dend = F,
                        show_column_dend = F,
                        show_row_names = T,
                        col = colorRampPalette(RColorBrewer::brewer.pal(9,"Blues"))(30),
                        name = "log2(q)",
)

GO enrichment

dotplot(new_IFNy.upregGO, split="ONTOLOGY", showCategory=10) + facet_grid(ONTOLOGY~., scale="free")

dotplot(new_IFNy.downregGO, split="ONTOLOGY", showCategory=10) + facet_grid(ONTOLOGY~., scale="free")

iMG vs ocMG

#==========iMG - COiMG correlation===============+#
#integrate iMG with COiMG
load('/Users/kubler01/Documents/R projects/bulk-org/data/Psamples_gene_matrix.RData')
genes_counts

meta_p <- data.frame(readxl::read_xlsx('/Users/kubler01/Documents/R projects/bulk-org/meta_P.xlsx'))
counts_p <- genes_counts[,colnames(genes_counts) %in% meta_p$ID]
counts_P.filt <- counts_p[rownames(counts_p) %in% counts_protcod2,]
rownames(counts_P.filt) <- make.unique(protcod_genes_reduced[,2])
#filter genes on expression
gene_names <- rownames(counts_P.filt)
cpm = edgeR::cpm(counts_P.filt)
median_genes_cpm <- enframe(rowMedians(as.matrix(counts_P.filt)), name = "Gene", value = "median_cpm")
median_genes_cpm <- cbind(median_genes_cpm, gene_names)
keep.exp <- dplyr::filter(median_genes_cpm, median_cpm > 1)
keep.exp <- keep.exp$gene_names

counts_P.filt2 <- counts_P.filt[keep.exp,]


dds.P <- DESeqDataSetFromMatrix(countData = round(counts_P.filt2),
                                   colData = meta_p,
                                   design = ~ RIN + `X260.280` + `X260.230` + type) # RIN + Line + Age + `260/230` + `260/280` + 

vsd.p <- vst(dds.P)
transformed.p <- data.frame(assay(vsd.p), check.names=F)


batch.p <- removeBatchEffect(transformed.p, 
                                covariates=as.matrix(cbind(
                                  meta_p$`X260.280`,
                                  meta_p$`X260.230`,
                                  meta_p$RIN)),
                                design=model.matrix(~ meta_p$type))


iMG <- batch.p[,colnames(batch.p) %in% meta_p[meta_p$type %in% 'iMG',]$ID]
ocMG <- batch.p[,colnames(batch.p) %in% meta_p[meta_p$type %in% 'ocMG',]$ID]
cor(iMG,ocMG)

##           P12       P13
## P10 1.0000000 0.9999866
## P11 1.0000000 0.9999866
## P9  0.9999866 1.0000000

df1 <- data.frame('iMG'=rowMeans(iMG))
df2 <- data.frame('ocMG'=rowMeans(ocMG))

df.plot <- cbind(df1,df2)

quantile(as.matrix(df1-df2))

##           0%          25%          50%          75%         100% 
## -1.517314803 -0.060528021  0.007527633  0.078884006  1.029107876

rownames(df.plot)[df1-df2 >= 0.5 | df1-df2 <= -1]

##  [1] "SCIN"     "NR1H3"    "APBA2"    "CDH1"     "RAI14"    "ELN"     
##  [7] "FAR2"     "NAV3"     "HES2"     "NUAK1"    "FSCN1"    "SLC1A3"  
## [13] "EPB41L2"  "OAS1"     "ITGA6"    "BLNK"     "PLTP"     "MXRA5"   
## [19] "PTGDS"    "ADGRD1"   "KIF20A"   "STEAP3"   "OTOF"     "CD207"   
## [25] "GBP1"     "SPP1"     "LTBP2"    "KCNJ5"    "KIAA1217" "KCNJ2"   
## [31] "AHNAK"    "MCF2L"    "CFP"      "SLC44A2"  "APOC1"    "COL5A1"  
## [37] "PPARG"    "CLEC10A"  "RIN2"     "ACY3"     "CHIT1"    "NTS"     
## [43] "ETS1"     "ADAM19"   "ITGA7"    "DYSF"     "GYPC"     "ANGPTL2" 
## [49] "IRF4"     "IFI44L"   "CHRNA1"   "RGS16"    "GRIP2"    "PTPRG"   
## [55] "MKI67"    "HS3ST3A1" "MYO1E"    "JAML"     "PLPP3"    "KIF26B"  
## [61] "FYCO1"    "MELTF"    "IFI27"    "HTRA1"    "CX3CR1"   "SOCS6"   
## [67] "SH3RF3"   "HSPB7"    "TP53I11"  "LRRN1"    "ALS2CL"   "PCED1B"  
## [73] "TSPAN10"  "CADM1"    "KCNQ3"    "INKA1"    "POU3F1"   "GLDN"    
## [79] "RYR1"     "GAL3ST4"  "PDGFA"    "ADGRG1"   "LILRA4"   "SIGLEC12"

Gene <- ifelse(rownames(df.plot) %in% rownames(df.plot)[df1-df2 >= 0.5 | df1-df2 <= -1], rownames(df.plot), "")

ggplot(df.plot, aes(x=iMG,y=ocMG))+
  geom_point() +
  labs(title = "Expression overlap iMG and ocMG",
       x = "Standardized iMG counts",
       y = "Standardized ocMG counts") +
  geom_smooth(method=lm,  linetype="dashed",
             color="darkred", fill="blue", se=TRUE) +
  geom_point(shape=18, color="grey")+
  stat_cor(method = "pearson", label.x = 0, label.y = 20)+
  geom_text_repel(data = df.plot, aes(x = iMG, y = ocMG, label = Gene), vjust = 3, size = 3) +
  theme_minimal()

IFNy in COiMG

Volcano plot

load("/Users/kubler01/Documents/R projects/bulk-org/01232024_figures AB_ifny in coimg.RData")


volcano_plot(data.frame(new_coimg_ifny), title = 'ctr vs IFNy in COiMG',
             annotate_by=top30.new_coimg_ifny, ymax1 = 80, ymax2 = 81, xmax1 = -5, xmax2 = 15) + 
  scale_x_continuous(breaks = c(-5,-3,-1, 0,1,3,5),
                     labels = c("-5",
                                '-3',
                                "-1",
                                "0",
                                "1",
                                '3',
                                "5"))

lFC correlation plot

lfc.plot <- data.frame('Gene'=new_IFNy$symbol,'CO'=new_IFNy$log2FoldChange,'COiMG'=new_coimg_ifny[rownames(new_coimg_ifny) %in% rownames(new_IFNy),]$log2FoldChange)
rownames(lfc.plot) <- new_IFNy$symbol

Gene <- ifelse(rownames(lfc.plot) %in% rownames(lfc.plot)[lfc.plot$CO-lfc.plot$COiMG >= 4 | lfc.plot$CO-lfc.plot$COiMG <= -4], rownames(lfc.plot), "")

ggplot(lfc.plot, aes(x=CO,y=COiMG))+
  geom_point() +
  labs(title = "lFC correlation in CO & COiMG under IFNy stimulation",
       x = "lFC IFNy in CO",
       y = "lFC IFNy in COiMG") +
  geom_smooth(method=lm,  linetype="dashed",
             color="darkred", fill="blue", se=TRUE) +
  geom_point(shape=18, color="grey")+
  stat_cor(method = "pearson", label.x = 0, label.y = 20)+
  geom_text_repel(data = lfc.plot, aes(x = CO, y = COiMG, label = Gene), vjust = 3, size = 3) +
  theme_minimal()

bulk-org figures (AB) V1.1

Raphael Kubler

01/23/2024

CO vs COiMG in control samples

PCA plots

Volcano plot

Boxplot genes of interest

Patir microglia genes heatmap

GO enrichment

IFNy CO vs COiMG

Stimulation (IFNy) samples

PCA plots

Volcano plot

Boxplot genes of interest

Top 40 DEGs heatmap

Enrichment heatmap (gene sets of interest)

NDD gene set enrichment in IFNy

GO enrichment

iMG vs ocMG

IFNy in COiMG

Volcano plot

lFC correlation plot